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Open AccessArticle

Multipass Target Search in Natural Environments

1
Institute for Systems Research, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
2
National Research Council RAP Postdoctoral Associate at Naval Research Laboratory, Washington, DC 20375, USA
3
Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC 20375, USA
4
Center for Advanced Manufacturing, Aerospace and Mechanical Engineering Department, University of Southern California, Los Angeles, CA 90089, USA
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2514; https://doi.org/10.3390/s17112514
Received: 14 August 2017 / Revised: 6 October 2017 / Accepted: 18 October 2017 / Published: 2 November 2017
(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)
Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ -admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time. View Full-Text
Keywords: path planning; information gathering; branch and bound; target search; coverage planning path planning; information gathering; branch and bound; target search; coverage planning
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MDPI and ACS Style

Kuhlman, M.J.; Otte, M.W.; Sofge, D.; Gupta, S.K. Multipass Target Search in Natural Environments. Sensors 2017, 17, 2514. https://doi.org/10.3390/s17112514

AMA Style

Kuhlman MJ, Otte MW, Sofge D, Gupta SK. Multipass Target Search in Natural Environments. Sensors. 2017; 17(11):2514. https://doi.org/10.3390/s17112514

Chicago/Turabian Style

Kuhlman, Michael J.; Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K. 2017. "Multipass Target Search in Natural Environments" Sensors 17, no. 11: 2514. https://doi.org/10.3390/s17112514

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